Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations45000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 MiB
Average record size in memory106.0 B

Variable types

Numeric8
Categorical4
Boolean10

Alerts

cb_person_cred_hist_length is highly overall correlated with person_age and 1 other fieldsHigh correlation
home_ownership_MORTGAGE is highly overall correlated with home_ownership_RENTHigh correlation
home_ownership_RENT is highly overall correlated with home_ownership_MORTGAGEHigh correlation
loan_amnt is highly overall correlated with loan_percent_incomeHigh correlation
loan_percent_income is highly overall correlated with loan_amntHigh correlation
loan_status is highly overall correlated with previous_loan_defaults_on_fileHigh correlation
person_age is highly overall correlated with cb_person_cred_hist_length and 1 other fieldsHigh correlation
person_emp_exp is highly overall correlated with cb_person_cred_hist_length and 1 other fieldsHigh correlation
previous_loan_defaults_on_file is highly overall correlated with loan_statusHigh correlation
home_ownership_OTHER is highly imbalanced (97.4%) Imbalance
home_ownership_OWN is highly imbalanced (65.1%) Imbalance
loan_intent_HOMEIMPROVEMENT is highly imbalanced (51.1%) Imbalance
person_income is highly skewed (γ1 = 34.13758313) Skewed
person_emp_exp has 9566 (21.3%) zeros Zeros

Reproduction

Analysis started2025-02-17 16:16:34.291170
Analysis finished2025-02-17 16:17:10.700367
Duration36.41 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

person_age
Real number (ℝ)

High correlation 

Distinct60
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.764178
Minimum20
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2025-02-17T17:17:11.025683image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile22
Q124
median26
Q330
95-th percentile39
Maximum144
Range124
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.0451082
Coefficient of variation (CV)0.2177305
Kurtosis18.649449
Mean27.764178
Median Absolute Deviation (MAD)3
Skewness2.548154
Sum1249388
Variance36.543333
MonotonicityNot monotonic
2025-02-17T17:17:11.651171image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 5254
11.7%
24 5138
11.4%
25 4507
10.0%
22 4236
9.4%
26 3659
 
8.1%
27 3095
 
6.9%
28 2728
 
6.1%
29 2455
 
5.5%
30 2021
 
4.5%
31 1645
 
3.7%
Other values (50) 10262
22.8%
ValueCountFrequency (%)
20 17
 
< 0.1%
21 1289
 
2.9%
22 4236
9.4%
23 5254
11.7%
24 5138
11.4%
25 4507
10.0%
26 3659
8.1%
27 3095
6.9%
28 2728
6.1%
29 2455
5.5%
ValueCountFrequency (%)
144 3
< 0.1%
123 2
< 0.1%
116 1
 
< 0.1%
109 1
 
< 0.1%
94 1
 
< 0.1%
84 1
 
< 0.1%
80 1
 
< 0.1%
78 1
 
< 0.1%
76 1
 
< 0.1%
73 3
< 0.1%

person_gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.7 KiB
1
24841 
0
20159 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 24841
55.2%
0 20159
44.8%

Length

2025-02-17T17:17:11.985470image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-17T17:17:12.230668image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
1 24841
55.2%
0 20159
44.8%

Most occurring characters

ValueCountFrequency (%)
1 24841
55.2%
0 20159
44.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 24841
55.2%
0 20159
44.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 24841
55.2%
0 20159
44.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 24841
55.2%
0 20159
44.8%

person_education
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.7 KiB
3
13399 
2
12028 
1
11972 
4
6980 
5
 
621

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row1
4th row3
5th row4

Common Values

ValueCountFrequency (%)
3 13399
29.8%
2 12028
26.7%
1 11972
26.6%
4 6980
15.5%
5 621
 
1.4%

Length

2025-02-17T17:17:12.489502image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-17T17:17:12.752519image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
3 13399
29.8%
2 12028
26.7%
1 11972
26.6%
4 6980
15.5%
5 621
 
1.4%

Most occurring characters

ValueCountFrequency (%)
3 13399
29.8%
2 12028
26.7%
1 11972
26.6%
4 6980
15.5%
5 621
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 13399
29.8%
2 12028
26.7%
1 11972
26.6%
4 6980
15.5%
5 621
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 13399
29.8%
2 12028
26.7%
1 11972
26.6%
4 6980
15.5%
5 621
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 13399
29.8%
2 12028
26.7%
1 11972
26.6%
4 6980
15.5%
5 621
 
1.4%

person_income
Real number (ℝ)

Skewed 

Distinct33989
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80319.053
Minimum8000
Maximum7200766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2025-02-17T17:17:13.062753image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum8000
5-th percentile28366.7
Q147204
median67048
Q395789.25
95-th percentile166754.7
Maximum7200766
Range7192766
Interquartile range (IQR)48585.25

Descriptive statistics

Standard deviation80422.499
Coefficient of variation (CV)1.0012879
Kurtosis2398.6848
Mean80319.053
Median Absolute Deviation (MAD)23124
Skewness34.137583
Sum3.6143574 × 109
Variance6.4677783 × 109
MonotonicityNot monotonic
2025-02-17T17:17:13.530389image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8000 15
 
< 0.1%
73011 10
 
< 0.1%
36995 9
 
< 0.1%
37020 8
 
< 0.1%
60914 8
 
< 0.1%
36946 7
 
< 0.1%
73040 7
 
< 0.1%
53638 7
 
< 0.1%
60864 7
 
< 0.1%
73082 7
 
< 0.1%
Other values (33979) 44915
99.8%
ValueCountFrequency (%)
8000 15
< 0.1%
8037 1
 
< 0.1%
8104 1
 
< 0.1%
8186 1
 
< 0.1%
8248 1
 
< 0.1%
8267 1
 
< 0.1%
8277 1
 
< 0.1%
8302 1
 
< 0.1%
8518 1
 
< 0.1%
9364 1
 
< 0.1%
ValueCountFrequency (%)
7200766 1
< 0.1%
5556399 1
< 0.1%
5545545 1
< 0.1%
2448661 1
< 0.1%
2280980 1
< 0.1%
2139143 1
< 0.1%
2012954 1
< 0.1%
1741243 1
< 0.1%
1728974 1
< 0.1%
1661567 1
< 0.1%

person_emp_exp
Real number (ℝ)

High correlation  Zeros 

Distinct63
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4103333
Minimum0
Maximum125
Zeros9566
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2025-02-17T17:17:13.868852image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q38
95-th percentile17
Maximum125
Range125
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.0635321
Coefficient of variation (CV)1.1207317
Kurtosis19.168324
Mean5.4103333
Median Absolute Deviation (MAD)3
Skewness2.5949174
Sum243465
Variance36.766421
MonotonicityNot monotonic
2025-02-17T17:17:14.485954image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9566
21.3%
2 4134
9.2%
1 4061
9.0%
3 3890
8.6%
4 3524
 
7.8%
5 3000
 
6.7%
6 2717
 
6.0%
7 2204
 
4.9%
8 1890
 
4.2%
9 1575
 
3.5%
Other values (53) 8439
18.8%
ValueCountFrequency (%)
0 9566
21.3%
1 4061
9.0%
2 4134
9.2%
3 3890
8.6%
4 3524
 
7.8%
5 3000
 
6.7%
6 2717
 
6.0%
7 2204
 
4.9%
8 1890
 
4.2%
9 1575
 
3.5%
ValueCountFrequency (%)
125 1
< 0.1%
124 1
< 0.1%
121 1
< 0.1%
101 1
< 0.1%
100 1
< 0.1%
93 1
< 0.1%
85 1
< 0.1%
76 1
< 0.1%
62 1
< 0.1%
61 1
< 0.1%

loan_amnt
Real number (ℝ)

High correlation 

Distinct4483
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9583.1576
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2025-02-17T17:17:14.906954image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2000
Q15000
median8000
Q312237.25
95-th percentile24000
Maximum35000
Range34500
Interquartile range (IQR)7237.25

Descriptive statistics

Standard deviation6314.8867
Coefficient of variation (CV)0.65895678
Kurtosis1.3512152
Mean9583.1576
Median Absolute Deviation (MAD)3800
Skewness1.1797313
Sum4.3124209 × 108
Variance39877794
MonotonicityNot monotonic
2025-02-17T17:17:15.648769image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 3617
 
8.0%
5000 2787
 
6.2%
6000 2426
 
5.4%
12000 2416
 
5.4%
15000 2004
 
4.5%
8000 1928
 
4.3%
4000 1406
 
3.1%
20000 1385
 
3.1%
3000 1378
 
3.1%
7000 1314
 
2.9%
Other values (4473) 24339
54.1%
ValueCountFrequency (%)
500 5
< 0.1%
563 1
 
< 0.1%
700 1
 
< 0.1%
725 1
 
< 0.1%
750 1
 
< 0.1%
800 1
 
< 0.1%
900 2
 
< 0.1%
912 1
 
< 0.1%
922 1
 
< 0.1%
950 1
 
< 0.1%
ValueCountFrequency (%)
35000 234
0.5%
34826 1
 
< 0.1%
34800 1
 
< 0.1%
34664 1
 
< 0.1%
34375 1
 
< 0.1%
34322 1
 
< 0.1%
34121 1
 
< 0.1%
34000 4
 
< 0.1%
33950 2
 
< 0.1%
33800 1
 
< 0.1%

loan_int_rate
Real number (ℝ)

Distinct1302
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.006606
Minimum5.42
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2025-02-17T17:17:16.536486image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum5.42
5-th percentile6.17
Q18.59
median11.01
Q312.99
95-th percentile16
Maximum20
Range14.58
Interquartile range (IQR)4.4

Descriptive statistics

Standard deviation2.9788083
Coefficient of variation (CV)0.27063823
Kurtosis-0.42033531
Mean11.006606
Median Absolute Deviation (MAD)2.13
Skewness0.21378407
Sum495297.26
Variance8.8732988
MonotonicityNot monotonic
2025-02-17T17:17:17.485142image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.01 3329
 
7.4%
10.99 804
 
1.8%
7.51 798
 
1.8%
7.49 687
 
1.5%
7.88 673
 
1.5%
5.42 608
 
1.4%
7.9 606
 
1.3%
11.49 514
 
1.1%
9.99 484
 
1.1%
13.49 475
 
1.1%
Other values (1292) 36022
80.0%
ValueCountFrequency (%)
5.42 608
1.4%
5.43 2
 
< 0.1%
5.44 2
 
< 0.1%
5.46 1
 
< 0.1%
5.47 5
 
< 0.1%
5.48 4
 
< 0.1%
5.49 4
 
< 0.1%
5.5 1
 
< 0.1%
5.51 3
 
< 0.1%
5.52 2
 
< 0.1%
ValueCountFrequency (%)
20 84
0.2%
19.91 9
 
< 0.1%
19.9 1
 
< 0.1%
19.82 5
 
< 0.1%
19.8 1
 
< 0.1%
19.79 4
 
< 0.1%
19.74 4
 
< 0.1%
19.69 12
 
< 0.1%
19.66 3
 
< 0.1%
19.62 1
 
< 0.1%

loan_percent_income
Real number (ℝ)

High correlation 

Distinct64
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13972489
Minimum0
Maximum0.66
Zeros27
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2025-02-17T17:17:18.230188image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.03
Q10.07
median0.12
Q30.19
95-th percentile0.31
Maximum0.66
Range0.66
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.087212308
Coefficient of variation (CV)0.6241716
Kurtosis1.0824162
Mean0.13972489
Median Absolute Deviation (MAD)0.05
Skewness1.0345122
Sum6287.62
Variance0.0076059867
MonotonicityNot monotonic
2025-02-17T17:17:18.803927image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08 2593
 
5.8%
0.1 2421
 
5.4%
0.07 2415
 
5.4%
0.09 2295
 
5.1%
0.06 2242
 
5.0%
0.12 2216
 
4.9%
0.05 2176
 
4.8%
0.11 2158
 
4.8%
0.14 1960
 
4.4%
0.04 1950
 
4.3%
Other values (54) 22574
50.2%
ValueCountFrequency (%)
0 27
 
0.1%
0.01 315
 
0.7%
0.02 944
 
2.1%
0.03 1488
3.3%
0.04 1950
4.3%
0.05 2176
4.8%
0.06 2242
5.0%
0.07 2415
5.4%
0.08 2593
5.8%
0.09 2295
5.1%
ValueCountFrequency (%)
0.66 1
 
< 0.1%
0.63 1
 
< 0.1%
0.62 2
 
< 0.1%
0.61 2
 
< 0.1%
0.59 1
 
< 0.1%
0.58 1
 
< 0.1%
0.57 1
 
< 0.1%
0.56 5
< 0.1%
0.55 5
< 0.1%
0.54 8
< 0.1%

cb_person_cred_hist_length
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8674889
Minimum2
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2025-02-17T17:17:19.336532image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median4
Q38
95-th percentile14
Maximum30
Range28
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.8797018
Coefficient of variation (CV)0.66122014
Kurtosis3.7259445
Mean5.8674889
Median Absolute Deviation (MAD)2
Skewness1.63172
Sum264037
Variance15.052086
MonotonicityNot monotonic
2025-02-17T17:17:19.797027image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4 8653
19.2%
3 8312
18.5%
2 6537
14.5%
5 3082
 
6.8%
6 2966
 
6.6%
7 2889
 
6.4%
8 2800
 
6.2%
9 2685
 
6.0%
10 2457
 
5.5%
12 715
 
1.6%
Other values (19) 3904
8.7%
ValueCountFrequency (%)
2 6537
14.5%
3 8312
18.5%
4 8653
19.2%
5 3082
 
6.8%
6 2966
 
6.6%
7 2889
 
6.4%
8 2800
 
6.2%
9 2685
 
6.0%
10 2457
 
5.5%
11 712
 
1.6%
ValueCountFrequency (%)
30 23
0.1%
29 15
< 0.1%
28 29
0.1%
27 23
0.1%
26 20
< 0.1%
25 23
0.1%
24 34
0.1%
23 26
0.1%
22 32
0.1%
21 24
0.1%

credit_score
Real number (ℝ)

Distinct340
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean632.60876
Minimum390
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size351.7 KiB
2025-02-17T17:17:20.297155image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum390
5-th percentile539
Q1601
median640
Q3670
95-th percentile703
Maximum850
Range460
Interquartile range (IQR)69

Descriptive statistics

Standard deviation50.435865
Coefficient of variation (CV)0.079726789
Kurtosis0.20302186
Mean632.60876
Median Absolute Deviation (MAD)33
Skewness-0.61026083
Sum28467394
Variance2543.7765
MonotonicityNot monotonic
2025-02-17T17:17:21.192952image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
658 406
 
0.9%
649 398
 
0.9%
652 396
 
0.9%
663 394
 
0.9%
647 393
 
0.9%
654 391
 
0.9%
650 391
 
0.9%
667 390
 
0.9%
653 390
 
0.9%
656 386
 
0.9%
Other values (330) 41065
91.3%
ValueCountFrequency (%)
390 1
 
< 0.1%
418 1
 
< 0.1%
419 1
 
< 0.1%
420 1
 
< 0.1%
421 1
 
< 0.1%
430 1
 
< 0.1%
431 2
< 0.1%
434 1
 
< 0.1%
435 4
< 0.1%
437 2
< 0.1%
ValueCountFrequency (%)
850 1
< 0.1%
807 1
< 0.1%
805 1
< 0.1%
792 1
< 0.1%
789 1
< 0.1%
784 2
< 0.1%
773 1
< 0.1%
772 1
< 0.1%
770 1
< 0.1%
768 1
< 0.1%

previous_loan_defaults_on_file
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.7 KiB
1
22858 
0
22142 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 22858
50.8%
0 22142
49.2%

Length

2025-02-17T17:17:21.663776image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-17T17:17:22.202900image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
1 22858
50.8%
0 22142
49.2%

Most occurring characters

ValueCountFrequency (%)
1 22858
50.8%
0 22142
49.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 22858
50.8%
0 22142
49.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 22858
50.8%
0 22142
49.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 22858
50.8%
0 22142
49.2%

loan_status
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size351.7 KiB
0
35000 
1
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 35000
77.8%
1 10000
 
22.2%

Length

2025-02-17T17:17:22.649220image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-17T17:17:23.002931image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
0 35000
77.8%
1 10000
 
22.2%

Most occurring characters

ValueCountFrequency (%)
0 35000
77.8%
1 10000
 
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 45000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 35000
77.8%
1 10000
 
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 45000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 35000
77.8%
1 10000
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 45000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 35000
77.8%
1 10000
 
22.2%

home_ownership_MORTGAGE
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
False
26511 
True
18489 
ValueCountFrequency (%)
False 26511
58.9%
True 18489
41.1%
2025-02-17T17:17:23.366557image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

home_ownership_OTHER
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
False
44883 
True
 
117
ValueCountFrequency (%)
False 44883
99.7%
True 117
 
0.3%
2025-02-17T17:17:23.740410image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

home_ownership_OWN
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
False
42049 
True
 
2951
ValueCountFrequency (%)
False 42049
93.4%
True 2951
 
6.6%
2025-02-17T17:17:24.051048image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

home_ownership_RENT
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
True
23443 
False
21557 
ValueCountFrequency (%)
True 23443
52.1%
False 21557
47.9%
2025-02-17T17:17:24.293408image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
False
37855 
True
7145 
ValueCountFrequency (%)
False 37855
84.1%
True 7145
 
15.9%
2025-02-17T17:17:24.565927image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
False
35847 
True
9153 
ValueCountFrequency (%)
False 35847
79.7%
True 9153
 
20.3%
2025-02-17T17:17:24.909461image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

loan_intent_HOMEIMPROVEMENT
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
False
40217 
True
4783 
ValueCountFrequency (%)
False 40217
89.4%
True 4783
 
10.6%
2025-02-17T17:17:25.514298image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
False
36452 
True
8548 
ValueCountFrequency (%)
False 36452
81.0%
True 8548
 
19.0%
2025-02-17T17:17:25.946530image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
False
37448 
True
7552 
ValueCountFrequency (%)
False 37448
83.2%
True 7552
 
16.8%
2025-02-17T17:17:26.298731image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size44.1 KiB
False
37181 
True
7819 
ValueCountFrequency (%)
False 37181
82.6%
True 7819
 
17.4%
2025-02-17T17:17:26.893874image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Interactions

2025-02-17T17:17:04.263418image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:41.417398image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:45.509628image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:48.624912image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:50.950969image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:53.703066image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:57.855407image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:17:00.714923image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:17:04.748219image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:41.828765image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:46.069057image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:48.892837image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:51.241394image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:53.977828image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:58.134576image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:17:00.962733image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:17:05.254801image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:42.361901image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:46.415273image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:49.203350image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:51.518130image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:54.246629image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:58.967630image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:17:01.266757image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:17:05.704797image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:42.988725image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:46.756001image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:49.480680image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:51.808095image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:54.538421image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:59.298742image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:17:01.752999image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:17:06.163383image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:43.296749image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:47.141663image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:49.817282image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:52.191521image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:54.822639image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:59.565313image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:17:02.285701image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:17:06.724824image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:43.580818image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:47.577663image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:50.193915image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:52.697878image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:55.176657image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:59.840662image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:17:02.562527image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:17:07.145112image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:44.341380image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:47.983258image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:50.442177image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:53.119918image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:56.021858image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:17:00.194887image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:17:03.229189image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:17:07.518632image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:45.012440image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:48.299014image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:50.703274image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:53.448019image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:16:57.347254image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:17:00.462779image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2025-02-17T17:17:03.702413image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Correlations

2025-02-17T17:17:27.143646image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
cb_person_cred_hist_lengthcredit_scorehome_ownership_MORTGAGEhome_ownership_OTHERhome_ownership_OWNhome_ownership_RENTloan_amntloan_int_rateloan_intent_DEBTCONSOLIDATIONloan_intent_EDUCATIONloan_intent_HOMEIMPROVEMENTloan_intent_MEDICALloan_intent_PERSONALloan_intent_VENTUREloan_percent_incomeloan_statusperson_ageperson_educationperson_emp_expperson_genderperson_incomeprevious_loan_defaults_on_file
cb_person_cred_hist_length1.0000.1420.0460.0110.0070.0430.0430.0170.0040.0860.0760.0230.0580.011-0.0370.0200.8210.0910.7500.0260.0930.026
credit_score0.1421.0000.0090.0000.0000.0060.0060.0110.0140.0320.0090.0000.0150.011-0.0120.0080.1600.1290.1720.0050.0230.178
home_ownership_MORTGAGE0.0460.0091.0000.0420.2210.8710.1500.1360.0150.0000.0500.0540.0110.0070.1560.2130.0240.0060.0140.0000.0200.115
home_ownership_OTHER0.0110.0000.0421.0000.0120.0530.0220.0240.0000.0030.0000.0000.0000.0050.0120.0120.0000.0040.0000.0000.0000.009
home_ownership_OWN0.0070.0000.2210.0121.0000.2760.0390.0160.0940.0000.0090.0150.0010.0910.0550.0930.0010.0060.0080.0000.0000.053
home_ownership_RENT0.0430.0060.8710.0530.2761.0000.1380.1410.0310.0000.0550.0610.0140.0370.1280.2550.0290.0090.0220.0000.0180.138
loan_amnt0.0430.0060.1500.0220.0390.1381.0000.1050.0150.0270.0500.0360.0170.0190.6660.1260.0640.0000.0520.0050.4050.066
loan_int_rate0.0170.0110.1360.0240.0160.1410.1051.0000.0040.0250.0310.0060.0000.0140.1240.3630.0130.0040.0160.000-0.0330.198
loan_intent_DEBTCONSOLIDATION0.0040.0140.0150.0000.0940.0310.0150.0041.0000.2190.1500.2100.1950.1990.0200.0840.0000.0040.0000.0000.0000.045
loan_intent_EDUCATION0.0860.0320.0000.0030.0000.0000.0270.0250.2191.0000.1740.2450.2270.2320.0160.0640.0420.0180.0370.0000.0100.038
loan_intent_HOMEIMPROVEMENT0.0760.0090.0500.0000.0090.0550.0500.0310.1500.1741.0000.1670.1550.1580.0230.0330.0340.0210.0330.0000.0000.021
loan_intent_MEDICAL0.0230.0000.0540.0000.0150.0610.0360.0060.2100.2450.1671.0000.2170.2220.0230.0650.0200.0080.0150.0040.0000.034
loan_intent_PERSONAL0.0580.0150.0110.0000.0010.0140.0170.0000.1950.2270.1550.2171.0000.2060.0080.0220.0420.0000.0460.0000.0180.000
loan_intent_VENTURE0.0110.0110.0070.0050.0910.0370.0190.0140.1990.2320.1580.2220.2061.0000.0110.0860.0210.0000.0200.0000.0180.052
loan_percent_income-0.037-0.0120.1560.0120.0550.1280.6660.1240.0200.0160.0230.0230.0080.0111.0000.415-0.0560.000-0.0500.000-0.3530.220
loan_status0.0200.0080.2130.0120.0930.2550.1260.3630.0840.0640.0330.0650.0220.0860.4151.0000.0120.0000.0140.0000.0090.543
person_age0.8210.1600.0240.0000.0010.0290.0640.0130.0000.0420.0340.0200.0420.021-0.0560.0121.0000.0600.8880.0240.1430.030
person_education0.0910.1290.0060.0040.0060.0090.0000.0040.0040.0180.0210.0080.0000.0000.0000.0000.0601.0000.0650.0000.0040.040
person_emp_exp0.7500.1720.0140.0000.0080.0220.0520.0160.0000.0370.0330.0150.0460.020-0.0500.0140.8880.0651.0000.0210.1200.028
person_gender0.0260.0050.0000.0000.0000.0000.0050.0000.0000.0000.0000.0040.0000.0000.0000.0000.0240.0000.0211.0000.0090.000
person_income0.0930.0230.0200.0000.0000.0180.405-0.0330.0000.0100.0000.0000.0180.018-0.3530.0090.1430.0040.1200.0091.0000.008
previous_loan_defaults_on_file0.0260.1780.1150.0090.0530.1380.0660.1980.0450.0380.0210.0340.0000.0520.2200.5430.0300.0400.0280.0000.0081.000

Missing values

2025-02-17T17:17:08.282147image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-17T17:17:09.884725image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

person_ageperson_genderperson_educationperson_incomeperson_emp_exploan_amntloan_int_rateloan_percent_incomecb_person_cred_hist_lengthcredit_scoreprevious_loan_defaults_on_fileloan_statushome_ownership_MORTGAGEhome_ownership_OTHERhome_ownership_OWNhome_ownership_RENTloan_intent_DEBTCONSOLIDATIONloan_intent_EDUCATIONloan_intent_HOMEIMPROVEMENTloan_intent_MEDICALloan_intent_PERSONALloan_intent_VENTURE
022.00471948.0035000.016.020.493.056101FalseFalseFalseTrueFalseFalseFalseFalseTrueFalse
121.00112282.001000.011.140.082.050410FalseFalseTrueFalseFalseTrueFalseFalseFalseFalse
225.00112438.035500.012.870.443.063501TrueFalseFalseFalseFalseFalseFalseTrueFalseFalse
323.00379753.0035000.015.230.442.067501FalseFalseFalseTrueFalseFalseFalseTrueFalseFalse
424.01466135.0135000.014.270.534.058601FalseFalseFalseTrueFalseFalseFalseTrueFalseFalse
521.00112951.002500.07.140.192.053201FalseFalseTrueFalseFalseFalseFalseFalseFalseTrue
626.00393471.0135000.012.420.373.070101FalseFalseFalseTrueFalseTrueFalseFalseFalseFalse
724.00195550.0535000.011.110.374.058501FalseFalseFalseTrueFalseFalseFalseTrueFalseFalse
824.002100684.0335000.08.900.352.054401FalseFalseFalseTrueFalseFalseFalseFalseTrueFalse
921.00112739.001600.014.740.133.064001FalseFalseTrueFalseFalseFalseFalseFalseFalseTrue
person_ageperson_genderperson_educationperson_incomeperson_emp_exploan_amntloan_int_rateloan_percent_incomecb_person_cred_hist_lengthcredit_scoreprevious_loan_defaults_on_fileloan_statushome_ownership_MORTGAGEhome_ownership_OTHERhome_ownership_OWNhome_ownership_RENTloan_intent_DEBTCONSOLIDATIONloan_intent_EDUCATIONloan_intent_HOMEIMPROVEMENTloan_intent_MEDICALloan_intent_PERSONALloan_intent_VENTURE
4499031.014136832.0912319.016.920.097.072201FalseFalseFalseTrueFalseFalseFalseFalseTrueFalse
4499124.01137786.0013500.013.430.364.061201TrueFalseFalseFalseFalseTrueFalseFalseFalseFalse
4499223.00340925.009000.011.010.224.048701FalseFalseFalseTrueFalseFalseFalseFalseTrueFalse
4499327.00135512.045000.015.830.145.050501FalseFalseFalseTrueFalseFalseFalseFalseTrueFalse
4499424.00231924.0212229.010.700.384.067801FalseFalseFalseTrueFalseFalseFalseTrueFalseFalse
4499527.01247971.0615000.015.660.313.064501FalseFalseFalseTrueFalseFalseFalseTrueFalseFalse
4499637.00265800.0179000.014.070.1411.062101FalseFalseFalseTrueFalseFalseTrueFalseFalseFalse
4499733.01256942.072771.010.020.0510.066801FalseFalseFalseTrueTrueFalseFalseFalseFalseFalse
4499829.01333164.0412000.013.230.366.060401FalseFalseFalseTrueFalseTrueFalseFalseFalseFalse
4499924.01151609.016665.017.050.133.062801FalseFalseFalseTrueTrueFalseFalseFalseFalseFalse